This document describes results from the OHI 2016 global assessment.
Scores are broken down as follows:
The following datasets and figures include the complete data for the OHI 2016 assessment.
Data files to generate content for the Ocean Health Index website.
Output files:
This carpet plot figure (download as high resolution png) provides a full overview of the scores from the 2016 assessment. Each row represents a region, the main groupings represent goals, and within each goal there are 5 years of data. Black regions indicate no data.
Don’t try too hard to interpret the results for specific countries/goals/years!!
This plot is good for providing a quick overview of things like:
Another resource that can be useful for examining scores is this interactive plot. This can be used to (some example screen shots):
explore the distribution of scores
compare different goal scores
observe change over time
This section describes global patterns in index and goal scores. If you are more interested in what is happening at the region scale, skip to the next section.
(NOTE: Livelihood and economy goals are not included here)
Overall, there weren’t dramatic changes across years.
The Index score for 2016 (eez area weighted average of region scores) was: 71. This value was essentially constant from 2012 to 2016.
I haven’t done a formal analysis, but it looks like over time:
| goal | 2012 | 2013 | 2014 | 2015 | 2016 |
|---|---|---|---|---|---|
| Index | 71.1 | 71.5 | 71.0 | 71.0 | 70.6 |
| Artisanal opportunities | 76.6 | 76.9 | 77.2 | 77.2 | 77.2 |
| Species condition (subgoal) | 91.1 | 91.1 | 91.1 | 91.2 | 91.2 |
| Biodiversity | 90.8 | 90.8 | 90.8 | 90.9 | 90.9 |
| Habitat (subgoal) | 90.4 | 90.5 | 90.5 | 90.5 | 90.6 |
| Coastal protection | 87.8 | 87.7 | 87.7 | 87.6 | 87.3 |
| Carbon storage | 79.2 | 79.2 | 79.2 | 79.2 | 79.3 |
| Clean water | 74.2 | 73.7 | 73.6 | 73.5 | 73.5 |
| Fisheries (subgoal) | 53.2 | 53.5 | 53.5 | 52.9 | 53.4 |
| Food provisioning | 53.3 | 53.6 | 53.7 | 52.9 | 53.2 |
| Mariculture (subgoal) | 30.1 | 32.8 | 33.7 | 32.8 | 31.9 |
| Iconic species (subgoal) | 66.2 | 67.4 | 67.4 | 67.8 | 66.5 |
| Sense of place | 61.1 | 62.6 | 62.6 | 63.1 | 62.4 |
| Lasting special places (subgoal) | 56.1 | 57.7 | 57.8 | 58.5 | 58.4 |
| Natural products | 59.0 | 58.0 | 56.5 | 53.2 | 48.0 |
| Tourism & recreation | 49.9 | 48.7 | 45.3 | 48.1 | 49.2 |
This section explores goal and index scores at the region level. I mostly focus on the 2016 scores.
This interactive table describes the index and goal scores for the regions in 2016 (and here’s a link to a color coded table, and a csv file can also be downloaded).
The median index score was 68. The highest score was 91 for Howland Island and Baker Island, and the lowest score was 44 for Sierra Leone.
The following histogram describes the distribution of overall index scores:
The regions with index scores of 80 or greater are:
| country | Index |
|---|---|
| Howland Island and Baker Island | 91 |
| Jarvis Island | 89 |
| South Georgia and the South Sandwich Islands | 88 |
| Christmas Island | 85 |
| Seychelles | 85 |
| Palmyra Atoll | 85 |
| Germany | 85 |
| Northern Saint-Martin | 84 |
| Cocos Islands | 83 |
| Phoenix Islands (Kiribati) | 83 |
| New Caledonia | 82 |
| Crozet Islands | 82 |
| Kerguelen Islands | 82 |
| Heard and McDonald Islands | 82 |
| Norfolk Island | 81 |
| Macquarie Island | 81 |
| Antigua and Barbuda | 81 |
| American Samoa | 81 |
| Aruba | 81 |
| Australia | 80 |
| Glorioso Islands | 80 |
The regions with index scores of 50 or less are:
| country | Index |
|---|---|
| Eritrea | 50 |
| Senegal | 50 |
| Lebanon | 50 |
| Algeria | 50 |
| Republique du Congo | 49 |
| Guinea Bissau | 49 |
| Liberia | 48 |
| Nicaragua | 47 |
| Democratic Republic of the Congo | 47 |
| Guinea | 45 |
| Ivory Coast | 45 |
| Libya | 44 |
| Sierra Leone | 44 |
A color-coded table of 5 year trends is available here (and a csv file).
These values are calculated using a linear model of scores for each region/goal over the past 5 years. Positive values indicate potentially increasing scores during the past 5 years and negative values indicate potentially decreasing scores.
NOTE: Currently, these data include the livelihoods and economies goal but this trends should probably be calculated without this goal.
The global average suggests a slight (but, probably negligible) decrease in index scores during the past five years (-0.11), but there was a good deal of variation among regions. The following histogram describes the distribution of trends in index scores:
The 10 regions with the largest increases in index scores are:
| country | trend |
|---|---|
| Mozambique | 2.46 |
| Samoa | 2.32 |
| Prince Edward Islands | 2.29 |
| South Georgia and the South Sandwich Islands | 2.22 |
| Tokelau | 2.19 |
| Solomon Islands | 2.05 |
| Tuvalu | 2.04 |
| Glorioso Islands | 2.04 |
| Tonga | 1.82 |
| Maldives | 1.80 |
The 10 regions with the largest decreases in index scores are:
| country | trend | |
|---|---|---|
| 211 | Saba | -2.42 |
| 212 | Ukraine | -2.46 |
| 213 | Sint Eustatius | -2.48 |
| 214 | Saudi Arabia | -2.56 |
| 215 | Norway | -2.56 |
| 216 | Sint Maarten | -2.59 |
| 217 | Finland | -2.63 |
| 218 | Equatorial Guinea | -2.98 |
| 219 | Estonia | -3.07 |
| 220 | Eritrea | -4.44 |
Here is a summary of the mean and standard deviation of the trends for different goals:
| goal | mean trend | sd trend |
|---|---|---|
| Index | -0.1320909 | 1.0972232 |
| Artisanal opportunities | 0.1703636 | 0.4172632 |
| Species condition (subgoal) | 0.0108182 | 0.0832187 |
| Biodiversity | -0.0203182 | 0.3694663 |
| Habitat (subgoal) | -0.0510138 | 0.7398197 |
| Coastal protection | -0.3630588 | 2.3081069 |
| Carbon storage | -0.0187838 | 0.0884785 |
| Clean water | -0.1435000 | 1.1309845 |
| Economies | 1.0139216 | 2.3537731 |
| Livelihoods & economies | 0.4516667 | 1.7634165 |
| Livelihoods | -0.1106863 | 2.0470061 |
| Fisheries (subgoal) | 0.5097727 | 2.1771129 |
| Food provisioning | 0.4900909 | 2.1767134 |
| Mariculture (subgoal) | 0.0564000 | 1.9816608 |
| Iconic species (subgoal) | 0.1383182 | 0.4453508 |
| Sense of place | 0.4508182 | 2.0043123 |
| Lasting special places (subgoal) | 0.7630000 | 3.9437809 |
| Natural products | -3.7691406 | 9.7389830 |
| Tourism & recreation | -0.3442647 | 3.7713096 |
These scores require a closer look, but possible patterns include:
The following is a comparison of the global status scores generated for 2015 by this year’s assessment vs. last year’s assessment.
If the models and source data remains the same, these scores should be exactly the same. Differences indicate changes in methods or source data (described in this downloadable document).
These changes do not reflect changes in actual system health!
The following goals had the largest changes:
The rest of the goals/subgoals changed by less than 3 points (on average, although regions might be highly variable).
| goal | assess2015 | assess2016 | change |
|---|---|---|---|
| Artisanal opportunities | 62.92 | 73.53 | 10.61 |
| Species condition (subgoal) | 85.18 | 92.78 | 7.60 |
| Biodiversity | 86.52 | 90.35 | 3.83 |
| Habitat (subgoal) | 87.90 | 87.91 | 0.01 |
| Coastal protection | 85.64 | 85.53 | -0.11 |
| Carbon storage | 78.42 | 78.42 | 0.00 |
| Clean water | 73.47 | 73.47 | 0.00 |
| Economies | 87.65 | 87.65 | 0.00 |
| Livelihoods & economies | 82.46 | 82.46 | 0.00 |
| Livelihoods | 77.28 | 77.28 | 0.00 |
| Fisheries (subgoal) | 54.91 | 50.48 | -4.43 |
| Food provisioning | 54.25 | 50.50 | -3.75 |
| Mariculture (subgoal) | 25.44 | 31.53 | 6.09 |
| Iconic species (subgoal) | 59.27 | 63.11 | 3.84 |
| Sense of place | 58.66 | 59.76 | 1.10 |
| Lasting special places (subgoal) | 58.54 | 56.41 | -2.13 |
| Natural products | 48.71 | 50.91 | 2.20 |
| Tourism & recreation | 48.29 | 46.98 | -1.31 |
This color-coded table compares the 2015 index scores generated for each region/goal between this year’s and last year’s assessment.
Because these are index scores, changes reflect updates to pressure and resilience scores as well as status.
The following interactive plot provides an overview of how the 2015 scores changed between the 2015 and 2016 assessment for all goals and dimensions.
Some general pattern in these data:
This section takes a closer look at each goal/subgoal. I do not include goals that are comprised of multiple subgoals, although the subgoals are described.
| country | score |
|---|---|
| Cayman Islands | 100 |
| United Arab Emirates | 100 |
| Qatar | 100 |
| Bermuda | 100 |
| United States | 100 |
| Kuwait | 100 |
| Norway | 100 |
| Ireland | 100 |
| Saudi Arabia | 100 |
| Brunei | 100 |
| country | score | |
|---|---|---|
| 211 | Ivory Coast | 46 |
| 212 | Solomon Islands | 45 |
| 213 | Cameroon | 45 |
| 214 | Mozambique | 44 |
| 215 | Comoro Islands | 44 |
| 216 | Madagascar | 44 |
| 217 | Benin | 44 |
| 218 | Togo | 43 |
| 219 | Guinea | 43 |
| 220 | Liberia | 42 |
| country | score |
|---|---|
| Northern Saint-Martin | 98 |
| Aruba | 98 |
| Curacao | 98 |
| Cayman Islands | 98 |
| Ascension | 98 |
| Bahamas | 98 |
| Saba | 98 |
| Montserrat | 98 |
| Bonaire | 98 |
| Turks and Caicos Islands | 98 |
| country | score | |
|---|---|---|
| 211 | Vietnam | 82 |
| 212 | Singapore | 82 |
| 213 | Eritrea | 82 |
| 214 | Oecussi Ambeno | 81 |
| 215 | Cambodia | 81 |
| 216 | East Timor | 81 |
| 217 | Libya | 81 |
| 218 | Myanmar | 80 |
| 219 | Sudan | 80 |
| 220 | Iraq | 78 |
| country | score |
|---|---|
| Kerguelen Islands | 100 |
| Heard and McDonald Islands | 100 |
| Norfolk Island | 100 |
| Macquarie Island | 100 |
| Tuvalu | 100 |
| Pitcairn | 100 |
| Wallis and Futuna | 100 |
| British Indian Ocean Territory | 100 |
| Suriname | 100 |
| Russia | 100 |
| country | score | |
|---|---|---|
| 208 | Colombia | 66 |
| 209 | Nigeria | 66 |
| 210 | Democratic Republic of the Congo | 64 |
| 211 | Belize | 62 |
| 212 | Jan Mayen | 61 |
| 213 | Senegal | 61 |
| 214 | Poland | 60 |
| 215 | Dominica | 60 |
| 216 | Sierra Leone | 59 |
| 217 | Iceland | 52 |
| country | score |
|---|---|
| Howland Island and Baker Island | 100 |
| Phoenix Islands (Kiribati) | 100 |
| Aruba | 100 |
| Curacao | 100 |
| Tuvalu | 100 |
| Pitcairn | 100 |
| French Polynesia | 100 |
| Wallis and Futuna | 100 |
| Netherlands | 100 |
| British Indian Ocean Territory | 100 |
| country | score | |
|---|---|---|
| 161 | Ivory Coast | 33 |
| 162 | Guinea | 31 |
| 163 | Sierra Leone | 31 |
| 164 | Senegal | 30 |
| 165 | Guinea Bissau | 30 |
| 166 | Nicaragua | 30 |
| 167 | Democratic Republic of the Congo | 30 |
| 168 | Lithuania | 29 |
| 169 | Dominica | 27 |
| 170 | Belize | 24 |
| country | score |
|---|---|
| Seychelles | 100 |
| Germany | 100 |
| Northern Saint-Martin | 100 |
| Antigua and Barbuda | 100 |
| Aruba | 100 |
| Netherlands | 100 |
| Bahamas | 100 |
| Suriname | 100 |
| Russia | 100 |
| South Africa | 100 |
| country | score | |
|---|---|---|
| 139 | Senegal | 34 |
| 140 | Liberia | 34 |
| 141 | Guinea | 33 |
| 142 | Ivory Coast | 33 |
| 143 | Sierra Leone | 33 |
| 144 | Guinea Bissau | 31 |
| 145 | Democratic Republic of the Congo | 30 |
| 146 | Barbados | 27 |
| 147 | Dominica | 27 |
| 148 | Nicaragua | 10 |
| country | score |
|---|---|
| Heard and McDonald Islands | 100 |
| South Georgia and the South Sandwich Islands | 99 |
| Kerguelen Islands | 99 |
| Falkland Islands | 99 |
| Bouvet Island | 99 |
| Jarvis Island | 98 |
| Macquarie Island | 98 |
| Howland Island and Baker Island | 97 |
| Phoenix Islands (Kiribati) | 97 |
| Crozet Islands | 97 |
| country | score | |
|---|---|---|
| 211 | Israel | 33 |
| 212 | Guatemala | 33 |
| 213 | Belgium | 32 |
| 214 | India | 29 |
| 215 | Benin | 29 |
| 216 | Lebanon | 29 |
| 217 | Slovenia | 28 |
| 218 | Togo | 28 |
| 219 | Monaco | 24 |
| 220 | Gibraltar | 20 |
| country | score |
|---|---|
| Tuvalu | 96 |
| Nauru | 96 |
| Palau | 95 |
| Mayotte | 91 |
| Oecussi Ambeno | 91 |
| Phoenix Islands (Kiribati) | 90 |
| Finland | 90 |
| Maldives | 89 |
| Seychelles | 88 |
| Solomon Islands | 88 |
| country | score | |
|---|---|---|
| 211 | El Salvador | 19 |
| 212 | Amsterdam Island and Saint Paul Island | 18 |
| 213 | Mauritania | 17 |
| 214 | Barbados | 17 |
| 215 | Western Sahara | 16 |
| 216 | Guinea | 16 |
| 217 | Wake Island | 12 |
| 218 | Turks and Caicos Islands | 9 |
| 219 | Jan Mayen | 6 |
| 220 | Bouvet Island | 3 |
| country | score |
|---|---|
| Russia | 100 |
| Ecuador | 100 |
| New Zealand | 100 |
| Norway | 100 |
| Chile | 100 |
| China | 100 |
| Faeroe Islands | 100 |
| Iceland | 99 |
| Belize | 94 |
| Canada | 72 |
| country | score | |
|---|---|---|
| 116 | Kenya | 0 |
| 117 | Jamaica | 0 |
| 118 | Nigeria | 0 |
| 119 | El Salvador | 0 |
| 120 | Pakistan | 0 |
| 121 | Eritrea | 0 |
| 122 | Senegal | 0 |
| 123 | Lebanon | 0 |
| 124 | Algeria | 0 |
| 125 | Libya | 0 |
| country | score |
|---|---|
| Cayman Islands | 100 |
| United Arab Emirates | 100 |
| Qatar | 100 |
| Bermuda | 100 |
| United States | 100 |
| Kuwait | 100 |
| Norway | 100 |
| Ireland | 100 |
| Saudi Arabia | 100 |
| Brunei | 100 |
| country | score | |
|---|---|---|
| 211 | Ivory Coast | 46 |
| 212 | Solomon Islands | 45 |
| 213 | Cameroon | 45 |
| 214 | Mozambique | 44 |
| 215 | Comoro Islands | 44 |
| 216 | Madagascar | 44 |
| 217 | Benin | 44 |
| 218 | Togo | 43 |
| 219 | Guinea | 43 |
| 220 | Liberia | 42 |
| country | score |
|---|---|
| Howland Island and Baker Island | 100 |
| Jarvis Island | 100 |
| South Georgia and the South Sandwich Islands | 100 |
| Palmyra Atoll | 100 |
| Germany | 100 |
| Northern Saint-Martin | 100 |
| Phoenix Islands (Kiribati) | 100 |
| Crozet Islands | 100 |
| Kerguelen Islands | 100 |
| Heard and McDonald Islands | 100 |
| country | score | |
|---|---|---|
| 211 | Bouvet Island | 0 |
| 212 | Benin | 0 |
| 213 | Iraq | 0 |
| 214 | Syria | 0 |
| 215 | Sudan | 0 |
| 216 | Somalia | 0 |
| 217 | North Korea | 0 |
| 218 | Eritrea | 0 |
| 219 | Liberia | 0 |
| 220 | Libya | 0 |
| country | score |
|---|---|
| New Caledonia | 100 |
| Suriname | 100 |
| Mozambique | 100 |
| India | 99 |
| Iran | 98 |
| French Polynesia | 97 |
| Trinidad and Tobago | 97 |
| Germany | 96 |
| Italy | 96 |
| Latvia | 96 |
| country | score | |
|---|---|---|
| 119 | Honduras | 0 |
| 120 | Brunei | 0 |
| 121 | Faeroe Islands | 0 |
| 122 | Cyprus | 0 |
| 123 | Sao Tome and Principe | 0 |
| 124 | Montenegro | 0 |
| 125 | Equatorial Guinea | 0 |
| 126 | Dominica | 0 |
| 127 | Algeria | 0 |
| 128 | Republique du Congo | 0 |
| country | score |
|---|---|
| Seychelles | 100 |
| Northern Saint-Martin | 100 |
| Antigua and Barbuda | 100 |
| Aruba | 100 |
| Malta | 100 |
| Curacao | 100 |
| Vanuatu | 100 |
| Maldives | 100 |
| Bahamas | 100 |
| Saba | 100 |
| country | score | |
|---|---|---|
| 195 | Democratic Republic of the Congo | 2 |
| 196 | Yemen | 0 |
| 197 | Ukraine | 0 |
| 198 | Iraq | 0 |
| 199 | Turkey | 0 |
| 200 | Syria | 0 |
| 201 | Somalia | 0 |
| 202 | North Korea | 0 |
| 203 | Lebanon | 0 |
| 204 | Libya | 0 |
This table describes the years of data that were used for each goal for each assessment year.
| goals | eez_2012 | eez_2013 | eez_2014 | eez_2015 | eez_2016 |
|---|---|---|---|---|---|
| AO | 2011 | 2012 | 2013 | 2014 | 2015 |
| SPP | NA | NA | NA | NA | NA |
| BD | NA | NA | NA | NA | NA |
| HAB | NA | NA | NA | NA | NA |
| CP | NA | NA | NA | NA | NA |
| CS | NA | NA | NA | NA | NA |
| CW | NA | NA | NA | NA | NA |
| ECO | 20122000 | 20122000 | 20122000 | 20122000 | 20122000 |
| LE | NA | NA | NA | NA | NA |
| LIV | 20102000 | 20112000 | 20122000 | 20122000 | 20122000 |
| FIS | 2006 | 2007 | 2008 | 2009 | 2010 |
| FP | NA | NA | NA | NA | NA |
| MAR | 2010 | 2011 | 2012 | 2013 | 2014 |
| ICO | 2012 | 2013 | 2014 | 2015 | 2016 |
| SP | NA | NA | NA | NA | NA |
| LSP | 2011 | 2012 | 2013 | 2014 | 2015 |
| NP | 2009 | 2010 | 2011 | 2012 | 2013 |
| TR | 2010 | 2011 | 2012 | 2013 | 2014 |
One thing we have been interested in is how well the likely future state score actually predicts the future score.
My initial conclusions are that the trend/pressure/resilience components of the likely future state score are not improving our predictions.
Methods: I compared the likely future status scores in 2012 to the observed status in 2016.
Results: At first glance, this appears promising because there is actaully a nice correlation between the values:
And, the slope estimate isn’t too far from 1 (0.76) and the R2 value is fairly high (0.75):
##
## Call:
## lm(formula = status_2016 ~ likely_future_state_2012, data = data_sp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.6357 -2.8018 0.1621 2.8615 11.0528
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.00566 2.01076 10.45 <2e-16 ***
## likely_future_state_2012 0.65202 0.02876 22.67 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.509 on 218 degrees of freedom
## Multiple R-squared: 0.7022, Adjusted R-squared: 0.7009
## F-statistic: 514.1 on 1 and 218 DF, p-value: < 2.2e-16
Methods: I compared the 2012 and 2016 status scores to get a feel for how well the 2012 scores predicted the 2016 scores.
Results: The 2012 scores alone do a better job predicting 2016 scores than incorporating the trend/pressure/resilience data. The additional information seems to, overall, make our predictions worse:
##
## Call:
## lm(formula = status_2016 ~ status_2012, data = data_sp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.9300 -1.4938 0.2677 1.7784 12.1589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.01133 2.09737 2.389 0.0177 *
## status_2012 0.92162 0.03143 29.323 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.716 on 218 degrees of freedom
## Multiple R-squared: 0.7977, Adjusted R-squared: 0.7968
## F-statistic: 859.8 on 1 and 218 DF, p-value: < 2.2e-16
Methods: Another way to look at these ata is to compare the predicted and observed changes in status from 2012 to 2016.
The predicted change in status was calculated as: status (2012) minus likely future score (2012). The observed change in score was calcualted as status (2012) minus status (2016).
Results: There was no correlation between the predicted change in status and the observed change in status:
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_sp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.9672 -1.5766 0.2428 2.0001 11.2743
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.33757 0.29703 -1.136 0.257
## pred_change 0.05436 0.05405 1.006 0.316
##
## Residual standard error: 3.76 on 218 degrees of freedom
## Multiple R-squared: 0.004617, Adjusted R-squared: 5.151e-05
## F-statistic: 1.011 on 1 and 218 DF, p-value: 0.3157
The next step in the above analysis is to look within each goal/subgoal to get a better feel for possible relationships between trend and pressure/resilience components.
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.4336 -0.4367 -0.1202 0.5684 4.0700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.43477 0.16011 2.715 0.00715 **
## pred_change 0.01732 0.01844 0.940 0.34850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.308 on 218 degrees of freedom
## Multiple R-squared: 0.004033, Adjusted R-squared: -0.0005358
## F-statistic: 0.8827 on 1 and 218 DF, p-value: 0.3485
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.6640 -0.4712 -0.1182 0.5061 4.6456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.330908 0.183614 1.802 0.07290 .
## trend_2012 8.510248 3.056197 2.785 0.00583 **
## r_minus_p 0.006277 0.004306 1.458 0.14634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.288 on 217 degrees of freedom
## Multiple R-squared: 0.03917, Adjusted R-squared: 0.03031
## F-statistic: 4.423 on 2 and 217 DF, p-value: 0.0131
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.6554 -0.4050 -0.0943 0.4460 4.7771
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.73704 1.10443 -1.573 0.11723
## trend_2012 8.11897 3.04500 2.666 0.00825 **
## pressures_2012 0.01166 0.01037 1.124 0.26218
## resilience_2012 0.02780 0.01212 2.294 0.02274 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.28 on 216 degrees of freedom
## Multiple R-squared: 0.05494, Adjusted R-squared: 0.04181
## F-statistic: 4.186 on 3 and 216 DF, p-value: 0.006625
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NA NA
## pred_change 0 0 NA NA
##
## Residual standard error: 0 on 218 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 218 DF, p-value: NA
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NA NA
## trend_2012 0 0 NA NA
## r_minus_p 0 0 NA NA
##
## Residual standard error: 0 on 217 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 2 and 217 DF, p-value: NA
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NA NA
## trend_2012 0 0 NA NA
## pressures_2012 0 0 NA NA
## resilience_2012 0 0 NA NA
##
## Residual standard error: 0 on 216 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 3 and 216 DF, p-value: NA
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.0323 -0.1645 0.1110 0.3672 15.0375
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.37627 0.18689 -2.013 0.04533 *
## pred_change 0.06486 0.02396 2.708 0.00732 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.148 on 215 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.03297, Adjusted R-squared: 0.02848
## F-statistic: 7.331 on 1 and 215 DF, p-value: 0.007323
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.8677 -0.0233 0.0844 0.1844 15.7936
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.206043 0.297167 -0.693 0.489
## trend_2012 1.496213 1.148257 1.303 0.194
## r_minus_p 0.004269 0.007678 0.556 0.579
##
## Residual standard error: 2.188 on 212 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.009962, Adjusted R-squared: 0.0006217
## F-statistic: 1.067 on 2 and 212 DF, p-value: 0.346
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.5544 -0.1430 0.0663 0.2980 16.0468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.45789 2.00787 1.224 0.222
## trend_2012 1.73076 1.15936 1.493 0.137
## pressures_2012 -0.02623 0.01808 -1.451 0.148
## resilience_2012 -0.02202 0.02104 -1.046 0.297
##
## Residual standard error: 2.184 on 211 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.01833, Adjusted R-squared: 0.004377
## F-statistic: 1.314 on 3 and 211 DF, p-value: 0.2709
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.506 0.665 0.872 1.239 20.996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.66474 0.54834 -1.212 0.227
## pred_change -0.07333 0.05887 -1.246 0.215
##
## Residual standard error: 6.556 on 168 degrees of freedom
## (50 observations deleted due to missingness)
## Multiple R-squared: 0.00915, Adjusted R-squared: 0.003252
## F-statistic: 1.551 on 1 and 168 DF, p-value: 0.2147
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.932 0.376 0.713 1.218 24.687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.508754 0.935732 -0.544 0.587
## trend_2012 -9.485762 2.254036 -4.208 4.41e-05 ***
## r_minus_p -0.009563 0.026235 -0.364 0.716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.575 on 150 degrees of freedom
## (67 observations deleted due to missingness)
## Multiple R-squared: 0.108, Adjusted R-squared: 0.09615
## F-statistic: 9.085 on 2 and 150 DF, p-value: 0.0001887
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.560 -0.485 0.634 1.734 26.384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.14953 5.11064 1.986 0.04887 *
## trend_2012 -7.83222 2.36072 -3.318 0.00114 **
## pressures_2012 -0.13325 0.07217 -1.846 0.06682 .
## resilience_2012 -0.09371 0.04740 -1.977 0.04991 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.5 on 149 degrees of freedom
## (67 observations deleted due to missingness)
## Multiple R-squared: 0.1342, Adjusted R-squared: 0.1167
## F-statistic: 7.697 on 3 and 149 DF, p-value: 8.139e-05
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NA NA
## pred_change 0 0 NA NA
##
## Residual standard error: 0 on 146 degrees of freedom
## (72 observations deleted due to missingness)
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 146 DF, p-value: NA
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NA NA
## trend_2012 0 0 NA NA
## r_minus_p 0 0 NA NA
##
## Residual standard error: 0 on 122 degrees of freedom
## (95 observations deleted due to missingness)
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 2 and 122 DF, p-value: NA
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NA NA
## trend_2012 0 0 NA NA
## pressures_2012 0 0 NA NA
## resilience_2012 0 0 NA NA
##
## Residual standard error: 0 on 121 degrees of freedom
## (95 observations deleted due to missingness)
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 3 and 121 DF, p-value: NA
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4848 -0.3783 -0.0283 0.1962 7.3090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.030160 0.079765 0.378 0.706
## pred_change -0.010548 0.008351 -1.263 0.208
##
## Residual standard error: 1.178 on 218 degrees of freedom
## Multiple R-squared: 0.007264, Adjusted R-squared: 0.002711
## F-statistic: 1.595 on 1 and 218 DF, p-value: 0.2079
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5176 -0.3544 -0.0330 0.2651 7.0581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.101058 0.093086 1.086 0.2788
## trend_2012 0.153175 0.528110 0.290 0.7721
## r_minus_p -0.005819 0.003129 -1.860 0.0643 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.175 on 217 degrees of freedom
## Multiple R-squared: 0.01602, Adjusted R-squared: 0.006956
## F-statistic: 1.767 on 2 and 217 DF, p-value: 0.1733
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4460 -0.3816 -0.0369 0.2376 7.0313
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.662778 0.596572 1.111 0.268
## trend_2012 0.117102 0.529575 0.221 0.825
## pressures_2012 0.001485 0.005519 0.269 0.788
## resilience_2012 -0.009948 0.005344 -1.861 0.064 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.176 on 216 degrees of freedom
## Multiple R-squared: 0.02015, Adjusted R-squared: 0.006538
## F-statistic: 1.48 on 3 and 216 DF, p-value: 0.2208
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37.285 -3.099 -0.290 1.872 39.356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0398 0.6562 1.585 0.115
## pred_change 0.1397 0.1080 1.294 0.197
##
## Residual standard error: 7.849 on 218 degrees of freedom
## Multiple R-squared: 0.007617, Adjusted R-squared: 0.003065
## F-statistic: 1.673 on 1 and 218 DF, p-value: 0.1972
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.113 -2.229 -0.591 1.097 38.550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.41789 0.83929 2.881 0.004363 **
## trend_2012 13.98954 3.97192 3.522 0.000522 ***
## r_minus_p -0.02157 0.02351 -0.917 0.360094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.655 on 217 degrees of freedom
## Multiple R-squared: 0.06054, Adjusted R-squared: 0.05188
## F-statistic: 6.991 on 2 and 217 DF, p-value: 0.001142
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.311 -2.317 -0.569 1.125 38.293
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.212763 7.281350 -0.029 0.976716
## trend_2012 14.329206 4.087981 3.505 0.000555 ***
## pressures_2012 0.039772 0.055324 0.719 0.472979
## resilience_2012 0.007554 0.083457 0.091 0.927965
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.67 on 216 degrees of freedom
## Multiple R-squared: 0.06111, Adjusted R-squared: 0.04807
## F-statistic: 4.686 on 3 and 216 DF, p-value: 0.003419
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.625 -0.245 1.026 1.043 40.021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.0359 0.6847 -1.513 0.133
## pred_change 0.7460 0.1517 4.917 2.75e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.075 on 123 degrees of freedom
## (95 observations deleted due to missingness)
## Multiple R-squared: 0.1643, Adjusted R-squared: 0.1575
## F-statistic: 24.18 on 1 and 123 DF, p-value: 2.75e-06
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.283 -1.703 0.001 1.631 59.025
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.00176 1.56065 0.642 0.5222
## trend_2012 2.20126 1.25171 1.759 0.0812 .
## r_minus_p -0.01993 0.03889 -0.512 0.6093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.687 on 121 degrees of freedom
## (96 observations deleted due to missingness)
## Multiple R-squared: 0.02952, Adjusted R-squared: 0.01348
## F-statistic: 1.84 on 2 and 121 DF, p-value: 0.1632
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.716 -2.063 -0.111 1.755 58.101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.72983 5.24639 1.664 0.0987 .
## trend_2012 2.54574 1.26453 2.013 0.0463 *
## pressures_2012 -0.07680 0.07369 -1.042 0.2994
## resilience_2012 -0.08913 0.05924 -1.505 0.1351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.644 on 120 degrees of freedom
## (96 observations deleted due to missingness)
## Multiple R-squared: 0.04838, Adjusted R-squared: 0.02459
## F-statistic: 2.034 on 3 and 120 DF, p-value: 0.1128
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.5406 -0.5394 -0.2818 0.5419 4.4507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.574520 0.231172 2.485 0.0137 *
## pred_change -0.007735 0.026319 -0.294 0.7691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.636 on 218 degrees of freedom
## Multiple R-squared: 0.000396, Adjusted R-squared: -0.004189
## F-statistic: 0.08637 on 1 and 218 DF, p-value: 0.7691
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.4913 -0.5583 -0.3083 0.5431 4.5245
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.460498 0.236643 1.946 0.053 .
## trend_2012 0.327503 3.005825 0.109 0.913
## r_minus_p 0.001393 0.006128 0.227 0.820
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.64 on 217 degrees of freedom
## Multiple R-squared: 0.0003137, Adjusted R-squared: -0.0089
## F-statistic: 0.03405 on 2 and 217 DF, p-value: 0.9665
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.5547 -0.5951 -0.2968 0.5700 4.5240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.625499 1.363389 -0.459 0.647
## trend_2012 0.279833 3.008800 0.093 0.926
## pressures_2012 0.006961 0.012012 0.580 0.563
## resilience_2012 0.012584 0.015135 0.831 0.407
##
## Residual standard error: 1.642 on 216 degrees of freedom
## Multiple R-squared: 0.003332, Adjusted R-squared: -0.01051
## F-statistic: 0.2407 on 3 and 216 DF, p-value: 0.8679
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.684 -3.687 -3.646 -3.646 96.354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.64627 1.14447 3.186 0.00165 **
## pred_change 0.02648 0.13589 0.195 0.84566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.09 on 218 degrees of freedom
## Multiple R-squared: 0.0001742, Adjusted R-squared: -0.004412
## F-statistic: 0.03798 on 1 and 218 DF, p-value: 0.8457
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.323 -5.469 -2.945 -0.965 94.902
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.27205 1.67929 0.757 0.4496
## trend_2012 -4.14306 2.99058 -1.385 0.1674
## r_minus_p 0.10928 0.04535 2.409 0.0168 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.89 on 217 degrees of freedom
## Multiple R-squared: 0.0313, Adjusted R-squared: 0.02237
## F-statistic: 3.506 on 2 and 217 DF, p-value: 0.03173
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.671 -5.193 -2.980 -0.478 92.485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.03748 7.93271 1.644 0.1017
## trend_2012 -5.36300 3.08814 -1.737 0.0839 .
## pressures_2012 -0.23553 0.09470 -2.487 0.0136 *
## resilience_2012 0.02003 0.07419 0.270 0.7875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.84 on 216 degrees of freedom
## Multiple R-squared: 0.04152, Adjusted R-squared: 0.02821
## F-statistic: 3.119 on 3 and 216 DF, p-value: 0.02699
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -88.236 -9.195 6.210 14.834 88.797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -11.7641 3.2845 -3.582 0.000486 ***
## pred_change -0.1747 0.2246 -0.778 0.438097
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 33.5 on 126 degrees of freedom
## (92 observations deleted due to missingness)
## Multiple R-squared: 0.004779, Adjusted R-squared: -0.003119
## F-statistic: 0.6051 on 1 and 126 DF, p-value: 0.4381
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.785 -10.850 1.616 18.371 90.589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.16424 7.84396 -1.041 0.29996
## trend_2012 -14.20002 4.24190 -3.348 0.00108 **
## r_minus_p -0.09716 0.17187 -0.565 0.57287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.23 on 125 degrees of freedom
## (92 observations deleted due to missingness)
## Multiple R-squared: 0.08596, Adjusted R-squared: 0.07134
## F-statistic: 5.878 on 2 and 125 DF, p-value: 0.003633
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -69.379 -14.572 1.445 21.109 76.628
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -176.6990 48.8153 -3.620 0.000428 ***
## trend_2012 -14.6324 4.0655 -3.599 0.000460 ***
## pressures_2012 2.0616 0.5858 3.519 0.000606 ***
## resilience_2012 1.4617 0.4755 3.074 0.002600 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30.88 on 124 degrees of freedom
## (92 observations deleted due to missingness)
## Multiple R-squared: 0.1679, Adjusted R-squared: 0.1478
## F-statistic: 8.34 on 3 and 124 DF, p-value: 4.277e-05
##
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.713 -4.751 0.566 3.269 43.557
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.6756 0.9277 -1.806 0.07240 .
## pred_change 0.3445 0.1038 3.320 0.00107 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.17 on 200 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.05223, Adjusted R-squared: 0.04749
## F-statistic: 11.02 on 1 and 200 DF, p-value: 0.00107
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.634 -5.071 -0.001 3.432 41.815
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.08879 1.14068 -0.955 0.34098
## trend_2012 7.98655 3.02678 2.639 0.00898 **
## r_minus_p 0.02212 0.03443 0.642 0.52146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.3 on 199 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.03531, Adjusted R-squared: 0.02561
## F-statistic: 3.641 on 2 and 199 DF, p-value: 0.02798
##
##
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012,
## data = data_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.673 -5.530 -0.070 3.738 41.609
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.23777 8.39340 2.292 0.0230 *
## trend_2012 5.41557 3.16935 1.709 0.0891 .
## pressures_2012 -0.24073 0.09570 -2.515 0.0127 *
## resilience_2012 -0.11101 0.06422 -1.729 0.0854 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.16 on 198 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.06355, Adjusted R-squared: 0.04936
## F-statistic: 4.479 on 3 and 198 DF, p-value: 0.004565